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Machine learning models for mitral valve replacement: A comparative analysis with the Society of Thoracic Surgeons risk score

Abstract:

Background Current Society of Thoracic Surgeons (STS) risk models for predicting outcomes of mitral valve surgery (MVS) assume a linear and cumulative impact of variables. We evaluated postoperative MVS outcomes and designed mortality and morbidity risk calculators to supplement the STS risk score. Methods Data from the STS Adult Cardiac Surgery Database for MVS was used from 2008 to 2017. The data included 383,550 procedures and 89 variables. Machine learning (ML) algorithms were employed t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1111/jocs.16072

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More by this author
Institution:
University of Oxford
Division:
SSD
Department:
Saïd Business School
Oxford college:
Exeter College
Role:
Author
ORCID:
0000-0003-3737-4826
More by this author
Role:
Author
ORCID:
0000-0002-1985-1003
Hartford HealthCare More from this funder
Publisher:
Wiley Publisher's website
Journal:
Journal of Cardiac Surgery Journal website
Volume:
37
Issue:
1
Pages:
18-28
Publication date:
2021-10-20
Acceptance date:
2021-10-05
DOI:
EISSN:
1540-8191
ISSN:
0886-0440
Pmid:
34669218
Language:
English
Keywords:
Pubs id:
1205867
Local pid:
pubs:1205867
Deposit date:
2021-12-16

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